

"""simple docstring"""




from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowercase   :	Optional[int]												=						logging.get_logger(__name__)
class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: str					=					["pixel_values"]



	def __init__(  self     :       Optional[Any]			,    _lowercase     :       bool = True			,    _lowercase     :       Dict[str, int] = None			,    _lowercase     :       PILImageResampling = PILImageResampling.BICUBIC			,    _lowercase     :       bool = True			,    _lowercase     :       Dict[str, int] = None			,    _lowercase     :       bool = True			,    _lowercase     :       Union[int, float] = 1 / 2_55			,    _lowercase     :       bool = True			,    _lowercase     :       Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN			,    _lowercase     :       Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD			,    **_lowercase     :       List[Any]			,    ):
		super().__init__(**_lowercase						)
		__UpperCAmelCase      		= size if size is not None else {'''shortest_edge''': 2_24}
		__UpperCAmelCase      		= get_size_dict(_lowercase			,    default_to_square=_lowercase						)
		__UpperCAmelCase      		= crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
		__UpperCAmelCase      		= get_size_dict(_lowercase			,    param_name='''crop_size'''						)

		__UpperCAmelCase      		= do_resize
		__UpperCAmelCase      		= size
		__UpperCAmelCase      		= resample
		__UpperCAmelCase      		= do_center_crop
		__UpperCAmelCase      		= crop_size
		__UpperCAmelCase      		= do_rescale
		__UpperCAmelCase      		= rescale_factor
		__UpperCAmelCase      		= do_normalize
		__UpperCAmelCase      		= image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
		__UpperCAmelCase      		= image_std if image_std is not None else IMAGENET_DEFAULT_STD



	def        a  (  self     :       Dict			,    _lowercase     :       np.ndarray			,    _lowercase     :       Dict[str, int]			,    _lowercase     :       PILImageResampling = PILImageResampling.BICUBIC			,    _lowercase     :       Optional[Union[str, ChannelDimension]] = None			,    **_lowercase     :       str			,    ):
		__UpperCAmelCase      		= get_size_dict(_lowercase			,    default_to_square=_lowercase						)
		# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
		if "shortest_edge" in size:
			__UpperCAmelCase      		= int((2_56 / 2_24) * size['''shortest_edge''']						)
			__UpperCAmelCase      		= get_resize_output_image_size(_lowercase			,    size=_lowercase			,    default_to_square=_lowercase						)
			__UpperCAmelCase      		= {'''height''': output_size[0], '''width''': output_size[1]}
		if "height" not in size_dict or "width" not in size_dict:
			raise ValueError(
			    F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''						)
		return resize(
		    _lowercase			,    size=(size_dict['''height'''], size_dict['''width'''])			,    resample=_lowercase			,    data_format=_lowercase			,    **_lowercase						)



	def        a  (  self     :       Union[str, Any]			,    _lowercase     :       np.ndarray			,    _lowercase     :       Dict[str, int]			,    _lowercase     :       Optional[Union[str, ChannelDimension]] = None			,    **_lowercase     :       Dict			,    ):
		__UpperCAmelCase      		= get_size_dict(_lowercase						)
		if "height" not in size or "width" not in size:
			raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
		return center_crop(_lowercase			,    size=(size['''height'''], size['''width'''])			,    data_format=_lowercase			,    **_lowercase						)



	def        a  (  self     :       str			,    _lowercase     :       np.ndarray			,    _lowercase     :       Union[int, float]			,    _lowercase     :       Optional[Union[str, ChannelDimension]] = None			,    **_lowercase     :       Union[str, Any]			,    ):
		return rescale(_lowercase			,    scale=_lowercase			,    data_format=_lowercase			,    **_lowercase						)



	def        a  (  self     :       Union[str, Any]			,    _lowercase     :       np.ndarray			,    _lowercase     :       Union[float, List[float]]			,    _lowercase     :       Union[float, List[float]]			,    _lowercase     :       Optional[Union[str, ChannelDimension]] = None			,    **_lowercase     :       Dict			,    ):
		return normalize(_lowercase			,    mean=_lowercase			,    std=_lowercase			,    data_format=_lowercase			,    **_lowercase						)



	def        a  (  self     :       Any			,    _lowercase     :       ImageInput			,    _lowercase     :       Optional[bool] = None			,    _lowercase     :       Optional[Dict[str, int]] = None			,    _lowercase     :       PILImageResampling = None			,    _lowercase     :       Optional[bool] = None			,    _lowercase     :       Optional[Dict[str, int]] = None			,    _lowercase     :       Optional[bool] = None			,    _lowercase     :       Optional[float] = None			,    _lowercase     :       Optional[bool] = None			,    _lowercase     :       Optional[Union[float, Iterable[float]]] = None			,    _lowercase     :       Optional[Union[float, Iterable[float]]] = None			,    _lowercase     :       Optional[TensorType] = None			,    _lowercase     :       ChannelDimension = ChannelDimension.FIRST			,    **_lowercase     :       Optional[Any]			,    ):
		__UpperCAmelCase      		= do_resize if do_resize is not None else self.do_resize
		__UpperCAmelCase      		= resample if resample is not None else self.resample
		__UpperCAmelCase      		= do_center_crop if do_center_crop is not None else self.do_center_crop
		__UpperCAmelCase      		= do_rescale if do_rescale is not None else self.do_rescale
		__UpperCAmelCase      		= rescale_factor if rescale_factor is not None else self.rescale_factor
		__UpperCAmelCase      		= do_normalize if do_normalize is not None else self.do_normalize
		__UpperCAmelCase      		= image_mean if image_mean is not None else self.image_mean
		__UpperCAmelCase      		= image_std if image_std is not None else self.image_std

		__UpperCAmelCase      		= size if size is not None else self.size
		__UpperCAmelCase      		= get_size_dict(_lowercase			,    default_to_square=_lowercase						)
		__UpperCAmelCase      		= crop_size if crop_size is not None else self.crop_size
		__UpperCAmelCase      		= get_size_dict(_lowercase			,    param_name='''crop_size'''						)

		__UpperCAmelCase      		= make_list_of_images(_lowercase						)

		if not valid_images(_lowercase						):
			raise ValueError(
			    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
			    '''torch.Tensor, tf.Tensor or jax.ndarray.'''						)

		if do_resize and size is None:
			raise ValueError('''Size must be specified if do_resize is True.'''						)

		if do_center_crop and crop_size is None:
			raise ValueError('''Crop size must be specified if do_center_crop is True.'''						)

		if do_rescale and rescale_factor is None:
			raise ValueError('''Rescale factor must be specified if do_rescale is True.'''						)

		if do_normalize and (image_mean is None or image_std is None):
			raise ValueError('''Image mean and std must be specified if do_normalize is True.'''						)

		# All transformations expect numpy arrays.
		__UpperCAmelCase      		= [to_numpy_array(_lowercase						) for image in images]

		if do_resize:
			__UpperCAmelCase      		= [self.resize(_lowercase			,    _lowercase			,    _lowercase						) for image in images]

		if do_center_crop:
			__UpperCAmelCase      		= [self.center_crop(_lowercase			,    _lowercase						) for image in images]

		if do_rescale:
			__UpperCAmelCase      		= [self.rescale(_lowercase			,    _lowercase						) for image in images]

		if do_normalize:
			__UpperCAmelCase      		= [self.normalize(_lowercase			,    _lowercase			,    _lowercase						) for image in images]

		__UpperCAmelCase      		= [to_channel_dimension_format(_lowercase			,    _lowercase						) for image in images]

		__UpperCAmelCase      		= {'''pixel_values''': images}
		return BatchFeature(data=_lowercase			,    tensor_type=_lowercase						)

def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)
		binary_number += "0" * shift_amount
		return binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)[2:]
		if shift_amount >= len(UpperCamelCase							):
				return "0b0"
		_lowerCamelCase				   =  binary_number[: len(UpperCamelCase							) - shift_amount]
		return "0b" + shifted_binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number >= 0:  # Get binary representation of positive number
				_lowerCamelCase				   =  '0' + str(bin(UpperCamelCase							)							).strip('-'							)[2:]
		else:  # Get binary (2's complement) representation of negative number
				_lowerCamelCase				   =  len(bin(UpperCamelCase							)[3:]							)  # Find 2's complement of number
				_lowerCamelCase				   =  bin(abs(UpperCamelCase							) - (1 << binary_number_length)							)[3:]
				_lowerCamelCase				   =  (
				    '1' + '0' * (binary_number_length - len(UpperCamelCase							)) + binary_number
				)

		if shift_amount >= len(UpperCamelCase							):
				return "0b" + binary_number[0] * len(UpperCamelCase							)
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(UpperCamelCase							) - shift_amount]
		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()